#Apache Kafka

Articles about Apache Kafka — exploring patterns, best practices, and real-world implementations in production systems.

1 post tagged with apache kafka. ← All posts

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Pratik Dhanave · ·7 min read

Stream Processing Systems — The Engines That Run It

The concepts of stream processing — events, time, windows, state, fault tolerance — are realized by a handful of systems, and choosing among them is one of the main practical decisions in building a streaming pipeline. This post maps the landscape: the engines, the models they embody, and how to think about which fits a problem. It's the bridge from understanding stream processing to actually running it.

The engines that implement stream processing: Flink (the heavyweight true stream processor), Spark Structured Streaming (micro-batch), Kafka Streams (library on Kafka), cloud-managed services, and the unifying Beam/Dataflow model. How to choose by state/correctness, ecosystem, ops capacity, and latency — plus the rise of streaming SQL.

#Stream Processing#Apache Flink#Apache Kafka#Data Engineering#Real-Time#Kafka#Streaming#Databases

All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.